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Review

Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review

by
Wan Muhd Hazwan Azamuddin
1,*,
Azana Hafizah Mohd Aman
1,
Hasimi Sallehuddin
1,
Maznifah Salam
1 and
Khalid Abualsaud
2
1
Faculty of Information Science & Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
2
Department of Computer Science & Engineering, College of Engineering, Qatar University, Doha 2713, Qatar
*
Author to whom correspondence should be addressed.
Mathematics 2024, 12(5), 649; https://doi.org/10.3390/math12050649
Submission received: 9 January 2024 / Revised: 8 February 2024 / Accepted: 18 February 2024 / Published: 23 February 2024
(This article belongs to the Section E1: Mathematics and Computer Science)

Abstract

One promising paradigm for content-centric communication is Named Data Networking (NDN), which revolutionizes data delivery and retrieval. A crucial component of NDN, producer mobility, presents new difficulties and opportunities for network optimization. This article reviews simulation strategies designed to improve NDN producer mobility. Producer mobility strategies have developed due to NDN data access needs, and these methods optimize data retrieval in dynamic networks. However, assessing their performance in different situations is difficult. Moreover, simulation approaches offer a cost-effective and controlled setting for experimentation, making them useful for testing these technologies. This review analyzes cutting-edge simulation methodologies for NDN producer mobility evaluation. These methodologies fall into three categories: simulation frameworks, mobility models, and performance metrics. Popular simulation platforms, including ns-3, OMNeT++, and ndnSIM, and mobility models that simulate producer movement are discussed. We also examine producer mobility performance indicators, such as handover data latency, signaling cost, and total packet loss. In conclusion, this comprehensive evaluation will help researchers, network engineers, and practitioners understand NDN producer mobility modeling approaches. By knowing these methodologies’ strengths and weaknesses, network stakeholders may make informed NDN solution development and deployment decisions, improving content-centric communication in dynamic network environments.
Keywords: named data networking; producer mobility; simulation methods; performance optimization; mobility models; ns-3; OMNeT++; ndnSIM; machine learning; artificial intelligence; performance metrics named data networking; producer mobility; simulation methods; performance optimization; mobility models; ns-3; OMNeT++; ndnSIM; machine learning; artificial intelligence; performance metrics

Share and Cite

MDPI and ACS Style

Azamuddin, W.M.H.; Mohd Aman, A.H.; Sallehuddin, H.; Salam, M.; Abualsaud, K. Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review. Mathematics 2024, 12, 649. https://doi.org/10.3390/math12050649

AMA Style

Azamuddin WMH, Mohd Aman AH, Sallehuddin H, Salam M, Abualsaud K. Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review. Mathematics. 2024; 12(5):649. https://doi.org/10.3390/math12050649

Chicago/Turabian Style

Azamuddin, Wan Muhd Hazwan, Azana Hafizah Mohd Aman, Hasimi Sallehuddin, Maznifah Salam, and Khalid Abualsaud. 2024. "Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review" Mathematics 12, no. 5: 649. https://doi.org/10.3390/math12050649

APA Style

Azamuddin, W. M. H., Mohd Aman, A. H., Sallehuddin, H., Salam, M., & Abualsaud, K. (2024). Mathematical Models for Named Data Networking Producer Mobility Techniques: A Review. Mathematics, 12(5), 649. https://doi.org/10.3390/math12050649

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